Question

我正在尝试训练两个流的CNN体系结构，但是不幸的是，我得到了一个意外的结果，该结果几乎与两个流之一相同（我已经分别训练了每个流）。我的想法是合并来自两个不同图像文件夹（两个来源）的两个流CNN，并将它们组合以获得更准确的结果。这是我的代码，请告诉我我做错了什么：

## Arch
    def fusionNet(nb_classes, inputs=(3, 224, 224)):
        input_img = Input(shape=inputs, name='RGB_input')
        conv1 = Conv2D(96, (7, 7), activation="relu", name="conv1", strides=(2, 2), padding="same", 
        ## ....
        ## ....
        ## ....
        firstCNN_output = BatchNormalization(name='firstCNN_output_BN')(last_dropout)
        ##
        print(firstCNN_output._keras_shape) # I got here (None, 512, 13, 13)



        input_img2 = Input(shape=inputs, name='depth_input')
        conv12 = Conv2D(96, (7, 7), activation="relu", name="conv12", strides=(2, 2), padding="same",             kernel_initializer="glorot_uniform")(input_img2)
        #....
        #....
        #...

        SecondCNN_output = BatchNormalization(name='SecondCNN_output_BN2')(last_dropout2)
        print(SecondCNN_output._keras_shape) # I got here (None, 512, 13, 13)
    #     

        merge2CNN = merge([firstCNN_output, SecondCNN_output], mode='concat', concat_axis=1)   
        ### got here (None, 1024, 13, 13) 
        conv10 = Conv2D(
            nb_classes, (1, 1),init='glorot_uniform',
            border_mode='valid', name='conv10')(merge2CNN)
            avgpool10 = AveragePooling2D((13, 13), name='avgpool10')(conv10)

        flatten = Flatten(name='flatten')(avgpool10)
        softmax = Activation("softmax", name='softmax')(flatten)

    return Model(inputs=[input_img,input_img2], output=softmax)

## Training

sn = fusionNet(nb_classes=24)
sgd = SGD(lr=0.001, decay=0.0002, momentum=0.9, nesterov=True)

sn.compile( optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy')

train_datagen = ImageDataGenerator(
rescale=1./255,
zoom_range=0.01,
rotation_range=2.,
featurewise_center=True,
featurewise_std_normalization=True,)
test_datagen = ImageDataGenerator(rescale=1./255)


def generate_generator_multiple(generator,dir1, dir2, batch_size,img_height,img_width):
    genX1 = generator.flow_from_directory(dir1,
                              target_size = (img_height,img_width),
                              class_mode = 'categorical',
                              batch_size = batch_size,
                              shuffle=False)

    genX2 = generator.flow_from_directory(dir2,
                              target_size = (img_height, img_width),
                              class_mode = 'categorical',
                              batch_size = batch_size)
    while True:
        X1i = genX1.next()
        X2i = genX2.next()
        yield [X1i[0], X2i[0]], X2i[1]  #Yield both images and their mutual label


train_generator=generate_generator_multiple(generator=train_datagen,
                               dir1=train_data_dir,
                               dir2=train_data_dir_colored,
                               batch_size=32,
                               img_height=size,
                               img_width=size)       

validation_generator=generate_generator_multiple(test_datagen,
                              dir1=validation_data_dir,
                              dir2=validation_data_dir_colored,
                              batch_size=batch_size,
                              img_height=size,
                              img_width=size)              



tensorboardcb =     keras.callbacks.TensorBoard(log_dir='/workspace/sqn_224/fusion',     histogram_freq=0,         batch_size=32, write_graph=True)
sn.fit_generator(
    train_generator,
steps_per_epoch=nb_train_samples//32,
nb_epoch=nb_epoch,
validation_data=validation_generator,
validation_steps = nb_validation_samples//32, 
callbacks=[tensorboardcb])

使用generator.flow_from_directory的多个CNN输入

0 个答案: